Search Results - Deep encoder–decoder architecture

Predicting membrane strains with a deep learning encoder-decoder convolutional neural network architecture trained on synthetic finite element data

Authors: B.S. Vien

Source: Materials Research Proceedings. 50:61-72

Local Change Detection from Video Scene: An End-to-End Encoder–Decoder-Type Deep Neural Network Architecture with Feature Pooling

Authors: Manoj Kumar Panda Badri Narayan Subudhi Thierry Bouwmans et al.

Source: Series in Computer Vision ISBN: 9789819807147

NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks

Authors: P. S. Arun Bibin Francis Varun P. Gopi

Source: IEEE Transactions on Artificial Intelligence. 6:700-709

Encoder-Decoder Architectures for Crack Detection on Surfaces: A Deep Learning Approach

Authors: null Sakshi Richa Vijay Sachin Lodhi et al.

Source: Ingénierie des systèmes d information. 30:495-503

Deep Learning Attitude Estimation Model Based on Encoder-Decoder Architecture

Authors: Xin Zhao Huixiang Liu Zhimin Zheng et al.

Source: 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). :627-631

Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance Images: Contribution to the HNTSMRG Challenge

Authors: Marek Wodzinski

Source: Lecture Notes in Computer Science ISBN: 9783031832734
Head Neck Tumor Segm MR Guid Appl (2024)

Subject Terms: Article

Access URL: https://pubmed.ncbi.nlm.nih.gov/40201773

Multi-Head Encoder-Decoder Deep Learning Architecture for Flood Segmentation and Mapping Through Multi-Sensor Data Fusion

Authors: Mulham Fawakherji Leila Hashemi-Beni

Source: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. :1191-1195

Automatic Multi-structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures

Authors: Marek Wodzinski Henning Müller

Source: Lecture Notes in Computer Science ISBN: 9783031889769

Discover the Tractable Latent Space of Floating Offshore Wind Turbine Based on a Novel GNN-Encoder-Decoder-LSTM Deep Learning Architecture

Authors: Kobe Hoi-Yin Yung Qing Xiao Xiuqing Xing et al.

Source: Lecture Notes in Computer Science ISBN: 9783031976254

Wsp-Extractor: A Generic Extraction Framework of Weld Seam Profiles Based on Deep Convolutional Encoder-Decoder Architecture for Automated Robotic Welding

Authors: Yinshui He Keyi Song Run Cai

DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

Authors: Sanjay Roka Manoj Diwakar

Source: Cluster Computing. 27:4925-4940

Fake news detection using a deep learning transformer based encoder-decoder architecture

Authors: M. Badri Narayanan Arun Kumar Ramesh K.S. Gayathri et al.

Source: Journal of Intelligent & Fuzzy Systems. 45:8001-8013

Subject Terms: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture for robotic environment sensing

Authors: Yuxia Yuan Yachao Zhang

Source: Journal of Intelligent & Fuzzy Systems. 45:2331-2345

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

CWRepViT-Net: An encoder-decoder deep learning framework with RepViT blocks for crop weed semantic segmentation in soybean fields through their life journey

Authors: Masoomeh Gomroki Dilshan Benaragama Christopher James Henry et al.

Source: Smart Agricultural Technology, Vol 12, Iss , Pp 101472- (2025)

Subject Terms: Crop and weed semantic segmentation, Soybean, Encoder-decoder architecture, RepViT, Precision agriculture, Agriculture (General), S1-972, Agricultural industries, HD9000-9495

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2772375525007038; https://doaj.org/toc/2772-3755

Access URL: https://doaj.org/article/65fcf468bd8a474588d2a70f2631dc70

Lightweight encoder-decoder architecture for Fat-Water separation in MRI using biophysical model-guided deep learning method

Authors: Ganeshkumar M Devasenathipathy Kandasamy Raju Sharma et al.

Source: ISMRM Annual Meeting.

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Authors: Abderrahim Norelyaqine Rida Azmi Abderrahim Saadane

Source: Scientific Programming. 2023:1-15

Subject Terms: 11. Sustainability, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

File Description: text/xhtml

Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

Authors: He Ming Yao Lijun Jiang Michael Ng

Source: IEEE Transactions on Antennas and Propagation. 71:1152-1157

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture

Authors: Km Poonam Rajlakshmi Guha Partha P Chakrabarti

Source: IEEE Journal of Biomedical and Health Informatics. 28:4184-4193

Subject Terms: Male, Deep Learning, Alzheimer Disease, Disease Progression, Humans, Female, Aged

Access URL: https://pubmed.ncbi.nlm.nih.gov/38593020

Enhanced encoder–decoder architecture for accurate monocular depth estimation.

Authors: Das, Dabbrata Das, Argho Deb Sadaf, Farhan

Source: Visual Computer. Sep2025, Vol. 41 Issue 12, p9487-9508. 22p.

Subject Terms: *DEPTH perception, *DEEP learning, *LOSS functions (Statistics), *FEATURE extraction

Encoder Decoder Based Deep Learning Architecture For Video Scene Parsing

Authors: Punam Sarmah Abhijit Gogoi Shobhanjana Kalita

Source: 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA). :1-6

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology